As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays...As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.展开更多
Edge computation offloading allows mobile end devices to execute compute-inten?sive tasks on edge servers. End devices can decide whether the tasks are offloaded to edge servers, cloud servers or executed locally acco...Edge computation offloading allows mobile end devices to execute compute-inten?sive tasks on edge servers. End devices can decide whether the tasks are offloaded to edge servers, cloud servers or executed locally according to current network condition and devic?es'profiles in an online manner. In this paper, we propose an edge computation offloading framework based on deep imitation learning (DIL) and knowledge distillation (KD), which assists end devices to quickly make fine-grained decisions to optimize the delay of computa?tion tasks online. We formalize a computation offloading problem into a multi-label classifi?cation problem. Training samples for our DIL model are generated in an offline manner. Af?ter the model is trained, we leverage KD to obtain a lightweight DIL model, by which we fur?ther reduce the model's inference delay. Numerical experiment shows that the offloading de?cisions made by our model not only outperform those made by other related policies in laten?cy metric, but also have the shortest inference delay among all policies.展开更多
Edge computation offloading has made some progress in the fifth generation mobile network(5G).However,load balancing in edge computation offloading is still a challenging problem.Meanwhile,with the continuous pursuit ...Edge computation offloading has made some progress in the fifth generation mobile network(5G).However,load balancing in edge computation offloading is still a challenging problem.Meanwhile,with the continuous pursuit of low execution latency in 5G multi-scenario,the functional requirements of edge computation offloading are further exacerbated.Given the above challenges,we raise a unique edge computation offloading method in 5G multi-scenario,and consider user satisfaction.The method consists of three functional parts:offloading strategy generation,offloading strategy update,and offloading strategy optimization.First,the offloading strategy is generated by means of a deep neural network(DNN),then update the offloading strategy by updating the DNN parameters.Finally,we optimize the offloading strategy based on changes in user satisfaction.In summary,compared to existing optimization methods,our proposal can achieve performance close to the optimum.Massive simulation results indicate the latency of the execution of our method on the CPU is under 0.1 seconds while improving the average computation rate by about 10%.展开更多
基金supported by Youth Talent Project of Scientific Research Program of Hubei Provincial Department of Education under Grant Q20241809Doctoral Scientific Research Foundation of Hubei University of Automotive Technology under Grant 202404.
文摘As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.
基金This work was supported in part by the National Science Foundation of China under Grant No.61972432the Program for Guangdong Introduc⁃ing Innovative and Entrepreneurial Teams under Grant No.2017ZT07X355.
文摘Edge computation offloading allows mobile end devices to execute compute-inten?sive tasks on edge servers. End devices can decide whether the tasks are offloaded to edge servers, cloud servers or executed locally according to current network condition and devic?es'profiles in an online manner. In this paper, we propose an edge computation offloading framework based on deep imitation learning (DIL) and knowledge distillation (KD), which assists end devices to quickly make fine-grained decisions to optimize the delay of computa?tion tasks online. We formalize a computation offloading problem into a multi-label classifi?cation problem. Training samples for our DIL model are generated in an offline manner. Af?ter the model is trained, we leverage KD to obtain a lightweight DIL model, by which we fur?ther reduce the model's inference delay. Numerical experiment shows that the offloading de?cisions made by our model not only outperform those made by other related policies in laten?cy metric, but also have the shortest inference delay among all policies.
基金This work was supported in part by the Science and Technology Project of North China University of Science and Technology under Grant ZD-YG-202317-23。
文摘Edge computation offloading has made some progress in the fifth generation mobile network(5G).However,load balancing in edge computation offloading is still a challenging problem.Meanwhile,with the continuous pursuit of low execution latency in 5G multi-scenario,the functional requirements of edge computation offloading are further exacerbated.Given the above challenges,we raise a unique edge computation offloading method in 5G multi-scenario,and consider user satisfaction.The method consists of three functional parts:offloading strategy generation,offloading strategy update,and offloading strategy optimization.First,the offloading strategy is generated by means of a deep neural network(DNN),then update the offloading strategy by updating the DNN parameters.Finally,we optimize the offloading strategy based on changes in user satisfaction.In summary,compared to existing optimization methods,our proposal can achieve performance close to the optimum.Massive simulation results indicate the latency of the execution of our method on the CPU is under 0.1 seconds while improving the average computation rate by about 10%.
